Search Results (516)

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Background/Objectives: In many parts of the world, pharmacists hold the primary responsibility for providing safe and effective pharmacotherapy. A key aspect is the availability of appropriate medicines for each individual patient. When industrially manufactured medicines are unsuitable or unavailable, pharmacists can prepare tailor-made medicines. While this principle applies globally, practices vary between countries. In the Netherlands, the preparation of medicines in pharmacies is well-established and integrated into routine healthcare. This narrative review explores the role and significance of extemporaneous compounding, pharmacy preparations and related product care in the Netherlands. Methods: Pharmacists involved in pharmacy preparations across various professional sectors, including community and hospital pharmacies, central compounding facilities, academia, and the professional pharmacists’ organisation, provided detailed and expert insights based on the literature and policy documents while also sharing their critical perspectives. Results: We present arguments supporting the need for pharmacy preparations and examine their position and role in community and hospital pharmacies in the Netherlands. Additional topics are discussed, including the regulatory and legal framework, outsourcing, quality assurance, standardisation, education, and international context. Specific pharmacy preparation topics, often with a research component and a strong focus on product care, are highlighted, including paediatric dosage forms, swallowing difficulties and feeding tubes, hospital-at-home care, reconstitution of oncolytic drugs and biologicals, total parenteral nutrition (TPN), advanced therapy medicinal products (ATMPs), radiopharmaceuticals and optical tracers, clinical trial medication, robotisation in reconstitution, and patient-centric solid oral dosage forms. Conclusions: The widespread acceptance of pharmacy preparations in the Netherlands is the result of a unique combination of strict adherence to tailored regulations that ensure quality and safety, and patient-oriented flexibility in design, formulation, and production. This approach is further reinforced by the standardisation of a broad range of formulations and procedures across primary, secondary and tertiary care, as well as by continuous research-driven innovation to develop new medicines, formulations, and production methods. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

Quality requirements for mineral aggregate for concrete used to construct pavement for busy highways are high because of the fatigue traffic loads and environmental exposure. The use of local aggregate for infrastructure projects could result in important sustainability improvements, provided that the concrete’s durability is assured. The objective of this study was to identify the potential alkaline reactivity of local greywacke aggregate and select appropriate mitigation measures against the alkali–silica reaction. Experimental tests on concrete specimens were performed using the miniature concrete prism test at 60 °C. Mixtures of coarse greywacke aggregate up to 12.5 mm with natural fine aggregate of different potential reactivity were evaluated in respect to the expansion, compressive strength, and elastic modulus of the concrete. Two preventive measures were studied—the use of metakaolin and slag-blended cement. A moderate reactivity potential of the greywacke aggregate was found, and the influence of reactive quartz sand on the expansion and instability of the mechanical properties of concrete was evaluated. Both crystalline and amorphous alkali–silica reaction products were detected in the cracks of the greywacke aggregate. Efficient expansion mitigation was obtained for the replacement of 15% of Portland cement by metakaolin or the use of CEM III/A cement with the slag content of 52%, even if greywacke aggregate was blended with moderately reactive quartz sand. It resulted in a relative reduction in expansion by 85–96%. The elastic modulus deterioration was less than 10%, confirming an increased stability of the elastic properties of concrete. Full article

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm⁻¹ (C–H stretching in lipids), ~1650 cm⁻¹ (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm⁻¹ range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products. Full article

This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. Full article

Surface defect segmentation based on deep learning has been widely applied in industrial inspection. However, two major challenges persist in specific application scenarios: first, the imbalanced area distribution between defects and the background leads to degraded segmentation performance; second, fine gaps within defects are prone to over-segmentation. To address these issues, this study proposes a two-stage image segmentation network that integrates a Defect Localization Module and a Pixel Confidence Module. In the first stage, the Defect Localization Module performs a coarse localization of defect regions and embeds the resulting feature vectors into the backbone of the second stage. In the second stage, the Pixel Confidence Module captures the probabilistic distribution of neighboring pixels, thereby refining the initial predictions. Experimental results demonstrate that the improved network achieves gains of $1.58\%\pm 0.80\%$ in $mPA$, $1.35\%\pm 0.77\%$ in $mIoU$ on the self-built Carbon Fabric Defect Dataset and $2.66\%\pm 1.12\%$ in $mPA$, $1.44\%\pm 0.79\%$ in $mIoU$ on the public Magnetic Tile Defect Dataset compared to the other network. These enhancements translate to more reliable automated quality assurance in industrial production environments. Full article

With the swift progression of the High-Standard Farmland Construction Program in China and worldwide, many dryland wheat fields can be irrigated once during the wheat growth stage (one-off irrigation). However, the combined strategies of one-off irrigation, tillage, and N management for augmenting wheat grain yield and quality are still undeveloped in drought regions. Two-site split–split field experiments were conducted to study the impacts of irrigation, tillage, and N management and their combined effects on grain yield; the contents of protein and protein components; processing quality; and the characteristics of N accumulation and translocation in wheat from a typical dryland wheat production area in China from 2020 to 2022. The irrigation practices (I0, zero irrigation and I1, one-off irrigation), tillage methods (RT, rotary tillage; PT, plowing; and SS, subsoiling) and N management (N0, N120, N180, and N240) were applied to the main plots, subplots and sub-subplots, respectively. The experimental sites, experimental years, irrigation practices, tillage methods, and N management methods and their interaction significantly affected the yield, quality, and plant N characteristics of wheat in most cases. Compared to zero irrigation, one-off irrigation significantly increased the plant N accumulation, enhancing grain yield by 33.7% while decreasing the contents of total protein, albumin, globulin, gliadin, and glutenin by 4.4%, 6.4%, 8.0%, 12.2%, and 10.0%, respectively. It also decreased the wet gluten content, stability time, sedimentation value, extensibility by 4.1%, 10.7%, 9.7%, and 5.5%, respectively, averaged across sites and years. Subsoiling simultaneously enhanced the aforementioned indicators compared to rotary tillage and plowing in most sites and years. With the increase in N rates, wheat yield firstly increased and then decreased under zero irrigation combined with rotary tillage, while it gradually increased when one-off irrigation was combined with subsoiling; however, the contents of total protein and protein components and the quality tended to increase firstly and then stabilize regardless of irrigation practices and tillage methods. The correlations of yield and quality indicators with plant N characteristics were negative when using distinct irrigation practices and tillage methods, while they were positive under varying N management. The decrease in wheat quality induced by one-off irrigation could be alleviated by optimizing N management. I1STN180 exhibited higher yield, plant N accumulation and translocation, and better quality in most cases; thus, all metrics of wheat quality were significantly increased, with a yield enhancement of 50.3% compared to I0RTN180. Therefore, one-off irrigation with subsoiling and an N rate of 180 kg ha⁻¹ is an optimal strategy for high yield, high protein, and high quality in dryland wheat production systems where one-off irrigation is assured. Full article

Increased market demand for plant herbs has prompted growers to ensure a continuous and assured supply of superior nutritional quality over the years. Apart from the nutritional value, culinary herbs contain phytochemical benefits that can improve human health. However, a significant amount of research has focused on enhancing yield, frequently overlooking the impact of production practices on the antioxidant and phytonutritional content of the produce. Thus, the study aimed to evaluate the yield, phytonutrients, and essential mineral profiling in selected aromatic herbs and their intricate role in nutritional quality when grown under different production systems. Five selected aromatic herbs (coriander, rocket, fennel, basil, and moss-curled parsley) were evaluated at harvest when grown under three production systems: in a gravel-film technique (GFT) hydroponic system and in soil, both under the 40% white shade-net structure, as well as in a soilless medium using sawdust under a non-temperature-controlled plastic tunnel (NTC). The phytonutritional quality properties (total phenolic, flavonoids, β-carotene-linoleic acid, and condensed tannins contents) as well as 1,1-diphenyl-2-picrylhydrazyl (DPPH) were assessed using spectrophotometry, while vitamin C and β-carotene were analyzed using HPLC-PDA, and leaf mineral content was evaluated using ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometry). The results show that the health benefits vary greatly owing to the particular culinary herb. The fresh leaf mass (yield) of coriander, parsley, and rocket was not significantly affected by the production system, whereas basil was high in soil cultivation, followed by GFT. Fennel had a high yield in the GFT system compared to in-soil and in-soilless cultivation. The highest levels of vitamin C were found in basil leaves grown in GFT and in soil compared to the soilless medium. The amount of total phenolic and flavonoid compounds, β-carotene, β-carotene-linoleic acid, and DPPH, were considerably high in soil cultivation, except on condensed tannins compared to the GFT and soilless medium, which could be a result of Photosynthetic Active Radiation (PAR) values (683 μmol/m²/s) and not favoring the accumulation of tannins. Overall, the mineral content was greatly influenced by the production system. Leaf calcium and magnesium contents were highly accumulated in rockets grown in the soilless medium and the GFT hydroponic system. The results have highlighted that growing environmental conditions significantly impact the accumulation of health-promoting phytonutrients in aromatic herbs. Some have positive ramifications, while others have negative ramifications. As a result, growers should prioritize in-soil production systems over GFT (under the shade-net) and soilless cultivation (under NTC) to produce aromatic herbs to improve the functional benefits and customer health. Full article

Automated quality assurance is gaining popularity across application areas; however, automatization for monitoring and control of product quality in waste processing is still in its infancy. At the same time, research on this topic is scattered, limiting efficient implementation of already developed strategies and technologies across research and application areas. To this end, the current work describes a scoping review conducted to systematically map available sensor-based quality assurance technologies and research based on the PRISMA-ScR framework. Additionally, the current state of research and potential automatization strategies are described in the context of construction and demolition waste processing. The results show 31 different sensor types extracted from a collection of 364 works, which have varied popularity depending on the application. However, visual imaging and spectroscopy sensors in particular seem to be popular overall. Only five works describing quality control system implementation were found, of which three describe varying manufacturing applications. Most works found describe proof-of-concept quality prediction systems on a laboratory scale. Compared to other application areas, works regarding construction and demolition waste processing indicate that the area seems to be especially behind in terms of implementing visual imaging at higher technology readiness levels. Moreover, given the importance of reliable and detailed data on material quality to transform the construction sector into a sustainable one, future research on quality monitoring and control systems could therefore focus on the implementation on higher technology readiness levels and the inclusion of detailed descriptions on how these systems have been verified. Full article

Microbiological contamination of food remains a critical global public health concern, contributing to millions of foodborne illness cases each year. Traditional diagnostic methods, particularly culture-based techniques, have been widely employed but are often limited by low sensitivity, insufficient specificity, and lengthy turnaround times. Recent advances in molecular biology, biosensor technology, and analytical chemistry have enabled the development of more rapid and precise diagnostic tools. Among these, Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has emerged as a transformative method for microbial identification. This review provides a comprehensive overview of the current applications of MALDI-TOF MS in the diagnosis of microbiological contamination in food. The method offers rapid, accurate, and cost-effective identification of microorganisms and is increasingly used in food safety laboratories for the detection of foodborne pathogens, ensuring the safety and quality of food products. We highlight the fundamental principles of MALDI-TOF MS, discuss its methodologies, and examine its advantages, limitations, and future prospects in food microbiology and quality assurance. Full article

The present study introduces an AI (Artificial Intelligence) framework for surface roughness assessment in milling operations through sound signal processing. As industrial demands escalate for in-process quality control solutions, the proposed system leverages audio data to estimate surface finish states without interrupting production. In order to address this, a novel classification approach was developed that maps audio waveform data into predictive indicators of surface quality. In particular, an experimental dataset was employed consisting of sound signals that were captured during milling procedures applying various machining conditions, where each signal was labeled with a corresponding roughness quality obtained via offline metrology. The formulated classification pipeline commences with audio acquisition, resampling, and normalization to ensure consistency across the dataset. These signals are then transformed into Mel-Frequency Cepstral Coefficients (MFCCs), which yield a compact time–frequency representation optimized for human auditory perception. Next, several AI algorithms were trained in order to classify these MFCCs into predefined surface roughness categories. Finally, the results of the work demonstrate that sound signals could contain sufficient discriminatory information enabling a reliable classification of surface finish quality. This approach not only facilitates in-process monitoring but also provides a foundation for intelligent manufacturing systems capable of real-time quality assurance. Full article

This paper presents an intelligent predictive system designed to support real-time decision making in the control of rubber blend mixing processes. The core of the system is a General Regression Neural Network (GRNN), which accurately predicts key process parameters, such as viscosity (expressed as torque), temperature, and energy consumption across varying masses of the processed material. The model can evaluate the mixing progress based on the initial 10% of input data, allowing early intervention and process optimisation. Experimental validation was conducted using a Brabender Plastograph EC Plus with a natural rubber-based blend in the mass range of 60–75 g. The GRNN kernel width parameter (σ) was optimised through a 10-fold cross-validation. High predictive accuracy was confirmed by values of the coefficient of determination (R²) approaching 1, and consistently low values of the root mean square error (RMSE). This system offers a robust and scalable solution for intelligent process control, productivity enhancement, and quality assurance across diverse industrial applications, beyond rubber blending. Full article

The transition to renewable energy requires sustainable solar manufacturing through optimized Production–Usage–Recycling (PUR) cycles, where electromagnetic (EM) sensing offers non-destructive monitoring solutions. This review categorizes EM methods into low- (<100 MHz) and medium-frequency (100 MHz–10 GHz) techniques for material evaluation, defect detection, and performance optimization throughout the solar lifecycle. During production, eddy current testing and impedance spectroscopy improve quality control while reducing waste. In operational phases, RFID-based monitoring enables continuous performance tracking and early fault detection of photovoltaic panels. For recycling, electrodynamic separation efficiently recovers materials, supporting circular economies. The analysis demonstrates the unique advantages of EM techniques in non-contact evaluation, real-time monitoring, and material-specific characterization, addressing critical sustainability challenges in photovoltaic systems. By examining capabilities and limitations, we highlight EM monitoring’s transformative potential for sustainable manufacturing, from production quality assurance to end-of-life material recovery. The frequency-based framework provides manufacturers with physics-guided solutions that enhance efficiency while minimizing environmental impact. This comprehensive assessment establishes EM technologies as vital tools for advancing solar energy systems, offering practical monitoring approaches that align with global sustainability goals. The review identifies current challenges and future opportunities in implementing these techniques, emphasizing their role in facilitating the renewable energy transition through improved resource efficiency and lifecycle management. Full article

Non-conformities—deviations from established standards or procedures—can significantly impact product quality and process performance. Although various tools and methodologies exist, current research lacks an integrated, deferred, and corrective approach to non-conformance management that bridges day-to-day operations with systematic quality control. The proposed tool aims to address this gap by providing a practical framework that combines batch data processing using the “Daily Challenge” tool with structured problem solving and corrective strategies. It serves as a comprehensive decision-making tool for systematically managing deviations. The methodology begins with identifying non-conformities through data collection and direct observation, followed by focused reporting and active discussion during departmental meetings. Issues are then categorized based on their frequency, operational impact, and resource requirements to determine the appropriate resolution path—whether through immediate correction or detailed analysis using structured tools such as the “Daily Challenge” sheet. It integrates well-established methodologies such as 5M and PDCA into a structured, daily workflow for resolving non-conformities. Implemented solutions are evaluated for effectiveness with ongoing monitoring to ensure continuous improvement. A key feature of this system is the use of the “Daily Challenge” form, which facilitates documentation, accountability, and knowledge retention—helping to reduce the recurrence of similar situations. The case studies illustrate the methodology through two examples: a labeling issue involving the omission of quantity information on product labels due to operator oversight and the management of production downtime caused by equipment and sensor failures. Although a standard existed, the errors revealed the need for reinforced procedures. Corrective actions included revising procedures, retraining personnel, repairing and recalibrating equipment, enhancing maintenance protocols, and using visual documentation to enhance process understanding. The “Daily Challenge” tool provides a replicable framework for managing non-conformities across various industries, aligning operational practices with quality assurance goals. By integrating structured analysis, clear documentation, and corrective strategies, it fosters a culture of continuous improvement and compliance. Full article